Introduction to the Wiley Series on Protein and Peptide Science |
Preface |
Contributors |
Introduction |
Why Are We Interested in the Unfolded Peptides and Proteins? / Vladimir N. Uversky ; A. Keith Dunker1: |
Why Study IDPs? / 1.1: |
Lesson 1: Disorderedness Is Encoded in the Amino Acid Sequence and Can Be Predicted / 1.3: |
Lesson 2: Disordered Proteins Are Highly Abundant in Nature / 1.4: |
Lesson 3: Disordered Proteins Are Globally Heterogeneous / 1.5: |
Lesson 4: Hydrodynamic Dimensions of Natively Unfolded Proteins Are Charge Dependent / 1.6: |
Lesson 5: Polymer Physics Explains Hydrodynamic Behavior of Disordered Proteins / 1.7: |
Lesson 6: Natively Unfolded Proteins Are Pliable and Very Sensitive to Their Environment / 1.8: |
Lesson 7: When Bound, Natively Unfolded Proteins Can Gain Unusual Structures / 1.9: |
Lesson 8: IDPs Can Form Disordered or Fuzzy Complexes / 1.10: |
Lesson 9: Intrinsic Disorder Is Crucial for Recognition, Regulation, and Signaling / 1.11: |
Lesson 10: Protein Posttranslational Modifications Occur at Disordered Regions / 1.12: |
Lesson 11: Disordered Regions Are Primary Targets for AS / 1.13: |
Lesson 12: Disordered Proteins Are Tightly Regulated in the Living Cells / 1.14: |
Lesson 13: Natively Unfolded Proteins Are Frequently Associated with Human Diseases / 1.15: |
Lesson 14: Natively Unfolded Proteins Are Attractive Drug Targets / 1.16: |
Lesson 15: Bright Future of Fuzzy Proteins / 1.17: |
Acknowledgments |
References |
Conformational Analysis of Unfolded States / I: |
Exploring the Energy Landscape of Small Peptides and Proteins by Molecular Dynamics Simulations / Gerhard Stock ; Abhinav Jain ; Laura Riccardi ; Phuong H. Nguyen2: |
Introduction: Free Energy Landscapes and How to Construct Them / 2.1: |
Dihedral Angle PCA Allows Us to Separate Internal and Global Motion / 2.2: |
Dimensionality of the Free Energy Landscape / 2.3: |
Characterization of the Free Energy Landscape: States, Barriers, and Transitions / 2.4: |
Low-Dimensional Simulation of Biomolecular Dynamics to Catch Slow and Rare Processes / 2.5: |
PCA by Parts: The Folding Pathways of Villin Headpiece / 2.6: |
The Energy Landscape of Aggregating Aβ-Peptides / 2.7: |
Concluding Remarks / 2.8: |
Local Backbone Preferences and Nearest-Neighbor Effects in the Unfolded and Native States / Joe DeBaitolo ; Abhishek Jha ; Karl F. Freed ; Tobin R. Sosnick3: |
Early Days: Random Coil-Theory and Experiment / 3.1: |
Denatured Proteins as Self-Avoiding Random Coils / 3.3: |
Modeling the Unfolded State / 3.4: |
NN Effects in Protein Structure Prediction / 3.5: |
Utilizing Folding Pathways for Structure Prediction / 3.6: |
Native State Modeling / 3.7: |
Secondary-Structure Propensities: Native Backbones in Unfolded Proteins / 3.8: |
Conclusions / 3.9: |
Short-Distance FRET Applied to the Polypeptide Chain / Maik H. Jacob ; Werner M. Nau4: |
A Short Timeline of Resonance Energy Transfer Applied to the Polypeptide Chain / 4.1: |
A Short Theory of FRET Applied to the Polypeptide Chain / 4.2: |
DBO and Dbo / 4.3: |
Short-Distance FRET Applied to the Structured Polypeptide Chain / 4.4: |
Short-Distance FRET to Monitor Chain-Structural Transitions upon Phosphorylation / 4.5: |
Short-Distance FRET Applied to the Structureless Chain / 4.6: |
The Future of Short-Distance FRET / 4.7: |
Dedication |
Solvation and Electrostatics as Determinants of Local Structural Order in Unfolded Peptides and Proteins / Franc Avbelj5: |
Local Structural Order in Unfolded Peptides and Proteins / 5.1: |
ESM / 5.2: |
The ESM and Strand-Coil Transition Model / 5.3: |
The ESM and Backbone Conformational Preferences / 5.4: |
The Nearest-Neighbor Effect / 5.5: |
The ESM and Cooperative Local Structures-Fluctuating β-Strands / 5.6: |
The ESM and β-Sheet Preferences in Native Proteins- Significance of Unfolded State / 5.7: |
The ESM and Secondary Chemical Shifts of Polypeptides / 5.8: |
Role of Backbone Solvation in Determining Hydrogen Exchange Rates of Unfolded Polypeptides / 5.9: |
Other Theoretical Models of Unfolded Polypeptides, 148 Acknowledgments / 5.10: |
Experimental and Computational Studies of Polyproline II Propensity / W. Austin Elam ; Travis P. Schrank ; Vincent J. Hilser6: |
Experimental Measurement of PII Propensities / 6.1: |
Computational Studies of Denatured State Conformational Propensities / 6.3: |
A Steric Model Reveals Common PII Propensity of the Peptide Backbone / 6.4: |
Correlation of PII Propensity to Amino Acid Properties / 6.5: |
Summary / 6.6: |
Mapping Conformational Dynamics in Unfolded Polypeptide Chains Using Short Model Peptides by NMR Spectroscopy / Daniel Mathieu ; Karin Rybka ; Jürgen Graf ; Harald Schwalbe7: |
General Aspects of NMR Spectroscopy / 7.1: |
NMR Parameters and Their Measurement / 7.3: |
Translating NMR Parameters to Structural Information / 7.4: |
Secondary Structure and Dynamics of a Family of Disordered Proteins / Pranesh Narayanaswami ; Gaiy W. Daughdrill7.5: |
Materials and Methods / 8.1: |
Results and Discussion / 8.3: |
Disordered Peptides and Molecular Recognition / II: |
Binding Promiscuity of Unfolded Peptides / Christopher J. Oldfield ; Bin Xue9: |
Protein-Protein Interaction Networks / 9.1: |
Role of Intrinsic Disorder in PPI Networks / 9.2: |
Transient Structural Elements in Protein-Based Recognition / 9.3: |
Chameleons and Adaptors: Binding Promiscuity of Unfolded Peptides / 9.4: |
Principles of Using the Unfolded Protein Regions for Binding / 9.5: |
Intrinsic Flexibility of Nucleic Acid Chaperone Proteins from Pathogenic RNA Viruses / Roland Ivanyi-Nagy ; Zuzanna Makowska ; Jean-Luc Darlix9.6: |
Retroviruses and Retroviral Nucleocapsid Proteins / 10.1: |
Core Proteins in the Flaviviridae Family of Viruses / 10.3: |
Coronavirus Nucleocapsid Protein / 10.4: |
Hantavirus Nucleocapsid Protein / 10.5: |
Aggregation of Disordered Peptides / III: |
Self-Assembling Alanine-Rich Peptides of Biomedical and Biotechnological Relevance / Thomas J. Measey ; Reinhard Schweitzer-Stenner11: |
Biomolecular Self-Assembly / 11.1: |
Misfolding and Human Disease / 11.2: |
Exploitation of Peptide Self-Assembly for Biotechnological Applications / 11.3: |
Structural Elements Regulating Interactions in the Early Stages of Fibrillogenesis: A Human Calcitonin Model System / Rosa Maria Vitale ; Giuseppina Andreotti ; Pietro Amodeo ; Andrea Motta11.4: |
Stating the Problem / 12.1: |
Aggregation Models: The State of The Art / 12.2: |
Human Calcitonin hCT as a Model System for Self-Assembly / 12.3: |
The "Prefibrillar" State of hCT / 12.4: |
How Many Molecules for the Critical Nucleus? / 12.5: |
Modeling Prefibrillar Aggregates / 12.6: |
hCT Helical Oligomers / 12.7: |
The Role of Aromatic Residues in the Early Stages of Amyloid Formation / 12.8: |
The Folding of hCT before Aggregation / 12.9: |
Model Explains the Differences in Aggregation Properties between hCT and sCT / 12.10: |
hCT Fibril Maturation / 12.11: |
α-Helix →β-Sheet Conformational Transition and hCT Fibrillation / 12.12: |
Solution NMR Studies of Aβ Monomers and Oligomers / Chunyu Wang12.13: |
Overexpression and Purification of Recombinant Aβ / 13.1: |
Aβ Monomers / 13.3: |
Aβ Oligomers and Monomer-Oligomer Interaction / 13.4: |
Conclusion / 13.5: |
Thermodynamic and Kinetic Models for Aggregation of Intrinsically Disordered Proteins / Scott L. Crick ; Rohit V. Pappu14: |
Thermodynamics of Protein Aggregation-the Phase Diagram Approach / 14.1: |
Thermodynamics of IDP Aggregation (Phase Separation)- MPM Description / 14.3: |
Kinetics of Homogeneous Nucleation and Elongation Using MPMs / 14.4: |
Concepts from Colloidal Science / 14.5: |
Modifiers of Protein Aggregation-From Nonspecific to Specific Interactions / Michal Levy-Sakin ; Roni Scherzer-Attali ; Ehud Gazit14.6: |
Nonspecific Modifiers / 15.1: |
Specific Modifiers / 15.3: |
Computational Studies of Folding and Assembly of Amyloidogenic Proteins / J. Srinivasa Rao ; Brigita Urbane ; Luis Cniz16: |
Amyloids / 16.1: |
Computer Simulations / 16.3: |
Index / 16.4: |
Introduction to the Wiley Series on Protein and Peptide Science |
Preface |
Contributors |
Introduction |
Why Are We Interested in the Unfolded Peptides and Proteins? / Vladimir N. Uversky ; A. Keith Dunker1: |
Why Study IDPs? / 1.1: |